In order to enhance ship detection accuracy and improve processing speed, an enhanced ship detection algorithm based on the YOLOv5 algorithm is introduced in this paper. Firstly, by adopting the Bidirectional Weighted Feature Pyramid Network, the model achieves higher accuracy in ship detection. Secondly, all the regular convolutions in the model are replaced with Ghost convolutions to achieve model lightweighting. The experimental results show that the average precision has been improved to 83.5%, exhibiting a 2.6 percentage point increase compared to the original model. The improved algorithm reduces the model's parameter size and computational complexity while maintaining high precision in ship detection.


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    Titel :

    Research on ship object detection based on deep learning


    Beteiligte:
    Yao, Xinwei (Herausgeber:in) / Kumar, Neeraj (Herausgeber:in) / Jin, Junkuan (Autor:in) / Xiao, Yingjie (Autor:in)

    Kongress:

    Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024) ; 2024 ; Beijin, China


    Erschienen in:

    Proc. SPIE ; 13160


    Erscheinungsdatum :

    16.05.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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